- 专利标题: COMPUTER-IMPLEMENTED METHOD FOR TRAINING OF A MACHINE LEARNING MODEL IN VEHICLE ACCIDENT EVENT ASSESSMENT
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申请号: EP23155637.4申请日: 2023-02-08
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公开(公告)号: EP4414222A1公开(公告)日: 2024-08-14
- 发明人: Ahmed, Jawwad , Alkhoury, Ziad
- 申请人: Autoliv Development AB
- 申请人地址: SE 447 83 Vårgårda Wallentinsvägen 22
- 专利权人: Autoliv Development AB
- 当前专利权人: Autoliv Development AB
- 当前专利权人地址: SE 447 83 Vårgårda Wallentinsvägen 22
- 代理机构: Westpatent AB
- 主分类号: B60R21/013
- IPC分类号: B60R21/013 ; G06N3/045
摘要:
The present disclosure relates to a computer-implemented method for training of a machine learning model in vehicle accident event assessment. The method comprises implementing (S100) the machine learning model, and preparing (S200) training data by obtaining and automatically acquiring (S210) sensor data generated from vehicle sensors (2, 3; 4, 5). The method further comprises applying data augmentation on the collected data to artificially increase the amount of collected data acquired from each sensor such that an augmented data set is acquired for each sensor.
For each sensor (2, 3; 4, 5), applying data augmentation (5220) comprises transforming (S221) the collected data to apply to a plurality of different sensor mounting positions and/or a plurality of different sensor mounting orientations within a certain sensor mounting area (6, 7), such that a further data set is obtained, the augmented data set comprising the collected data and the further data set. The method further comprises training (S300) the machine learning model using the augmented data set for each sensor (2, 3; 4, 5).
For each sensor (2, 3; 4, 5), applying data augmentation (5220) comprises transforming (S221) the collected data to apply to a plurality of different sensor mounting positions and/or a plurality of different sensor mounting orientations within a certain sensor mounting area (6, 7), such that a further data set is obtained, the augmented data set comprising the collected data and the further data set. The method further comprises training (S300) the machine learning model using the augmented data set for each sensor (2, 3; 4, 5).
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